Mathematical model, forecast and analysis on the spread of COVID-19.

Pandemic COVID-19 which has infected more than 35,027,546 people and death more than 1,034,837 people in 235 countries as on October 05, 2020 has created a chaos across the globe. In this paper, we develop a compartmental epidemic model to understand the spreading behaviour of the disease in human population with a special case of Bhilwara, a desert town in India where successful control measures TTT (tracking, testing and treatment) was adopted to curb the disease in the very early phase of the spread of the disease in India. Local and global asymptotic stability is established for endemic equilibrium.

COVID-19 created chaos across the globe: Three novel quarantine epidemic models.

The latest version of human coronavirus said to be COVID-19 came out as a sudden pandemic disease within human population and in the absence of vaccination and proper treatment till date, it daunting threats heavily to human lives, infecting more than 12, 11, 214 people and death more than 67, 666 people in 208 countries across the globe as on April 06, 2020, which is highly alarming. When no treatment or vaccine is available till date and to avoid COVID-19 to be transmitted in the community, social distancing is the only way to prevent the disease, which is well taken into account in our novel epidemic models as a special compartment, that is, home isolation. Based on the transmitting behavior of COVID-19 in the human population, we develop three quarantine models of this pandemic taking into account the compartments: susceptible population, immigrant population, home isolation population, infectious population, hospital quarantine population, and recovered population.

Impact of Information based Classification on Network Epidemics.

Formulating mathematical models for accurate approximation of malicious propagation in a network is a difficult process because of our inherent lack of understanding of several underlying physical processes that intrinsically characterize the broader picture. The aim of this paper is to understand the impact of available information in the control of malicious network epidemics. A 1-n-n-1 type differential epidemic model is proposed, where the differentiality allows a symptom based classification.